The Problem With Mechanistic Risk of Bias Assessments in Evidence Synthesis of Observational Studies and a Practical Alternative: Assessing the Impact of Specific Sources of Potential Bias

Abstract

Abstract The trustworthiness of individual studies is routinely characterized in systemic reviews by evaluating risk of bias, often by mechanistically applying standardized algorithms. However, such instruments prioritize the repeatability of the process over a more thoughtful and informative but necessarily somewhat more subjective approach. In mechanistic risk of bias assessments, the focus is on determining whether specific biases are present, but these assessments do not provide insights into the direction, magnitude, and relative importance of individual biases. In such assessments, all potential biases are naively treated as equally important threats to validity and equally important across all research topics, potentially leading to inappropriate conclusions about the overall strength of the available evidence. Instead, risk of bias assessments be should focused on identifying a few of the most likely influential sources of bias, based on methodologic and subject matter expertise, classifying each specific study on the basis of on how effectively it has addressed each potential bias, and determining whether results differ across studies in relation to susceptibility to each hypothesized source of bias. This approach provides insight into the potential impact of each specific bias, identifies a subset of studies likely to best approximate the causal effect, and suggests design features needed to improve future research.